Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm
Summary :
An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.11 pp.3534-3543
- Publication Date
- 2008/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e91-b.11.3534
- Type of Manuscript
- Special Section PAPER (Special Section on Emerging Technologies for Practical Ubiquitous and Sensor Networks)
- Category
Authors
Keyword
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Jae-Hyun SEO, Yong-Hyuk KIM, Hwang-Bin RYOU, Si-Ho CHA, Minho JO, "Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 11, pp. 3534-3543, November 2008, doi: 10.1093/ietcom/e91-b.11.3534.
Abstract: An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.11.3534/_p
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@ARTICLE{e91-b_11_3534,
author={Jae-Hyun SEO, Yong-Hyuk KIM, Hwang-Bin RYOU, Si-Ho CHA, Minho JO, },
journal={IEICE TRANSACTIONS on Communications},
title={Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm},
year={2008},
volume={E91-B},
number={11},
pages={3534-3543},
abstract={An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.},
keywords={},
doi={10.1093/ietcom/e91-b.11.3534},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 3534
EP - 3543
AU - Jae-Hyun SEO
AU - Yong-Hyuk KIM
AU - Hwang-Bin RYOU
AU - Si-Ho CHA
AU - Minho JO
PY - 2008
DO - 10.1093/ietcom/e91-b.11.3534
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2008
AB - An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.
ER -
English
